A hybrid conjugate gradient based approach for solving unconstrained optimization and motion control problems

In this article, we propose a hybrid conjugate gradient (CG) scheme for solving unconstrained optimization problem. The search direction is a combination of the Polak–Ribière–Polyak (PRP) and the Liu–Storey (LS) CG parameters and is close to the direction of the memoryless Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton scheme. Without the use of the line search, the search direction satisfies the descent condition and possesses the trust region property. The global convergence of the scheme for general functions under the Wolfe-type and Armijo-type line search is established. Numerical experiments are carried out on some benchmark test problems and the results show that the propose scheme is more efficient than other existing schemes. Finally, a practical application of the scheme in motion control of robot manipulator is also presented.